#!/usr/bin/env python
# coding=utf-8

# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
# Copyright 2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================

import tensorflow as tf
import numpy as np
import tensorflow.contrib.slim as slim

def resblock(inputs, out_channel=32, name='resblock'):
    
    with tf.variable_scope(name):
        
        x = slim.convolution2d(inputs, out_channel, [3, 3], 
                               activation_fn=None, scope='conv1')
        x = tf.nn.leaky_relu(x)
        x = slim.convolution2d(x, out_channel, [3, 3], 
                               activation_fn=None, scope='conv2')
        
        return x + inputs




def unet_generator(inputs, channel=32, num_blocks=4, name='generator', reuse=False):
    with tf.variable_scope(name, reuse=reuse):
        
        x0 = slim.convolution2d(inputs, channel, [7, 7], activation_fn=None)
        x0 = tf.nn.leaky_relu(x0)
        
        x1 = slim.convolution2d(x0, channel, [3, 3], stride=2, activation_fn=None)
        x1 = tf.nn.leaky_relu(x1)
        x1 = slim.convolution2d(x1, channel*2, [3, 3], activation_fn=None)
        x1 = tf.nn.leaky_relu(x1)
        
        x2 = slim.convolution2d(x1, channel*2, [3, 3], stride=2, activation_fn=None)
        x2 = tf.nn.leaky_relu(x2)
        x2 = slim.convolution2d(x2, channel*4, [3, 3], activation_fn=None)
        x2 = tf.nn.leaky_relu(x2)
        
        for idx in range(num_blocks):
            x2 = resblock(x2, out_channel=channel*4, name='block_{}'.format(idx))
            
        x2 = slim.convolution2d(x2, channel*2, [3, 3], activation_fn=None)
        x2 = tf.nn.leaky_relu(x2)
        
        h1, w1 = tf.shape(x2)[1], tf.shape(x2)[2]
        x3 = tf.image.resize_bilinear(x2, (h1*2, w1*2))
        x3 = slim.convolution2d(x3+x1, channel*2, [3, 3], activation_fn=None)
        x3 = tf.nn.leaky_relu(x3)
        x3 = slim.convolution2d(x3, channel, [3, 3], activation_fn=None)
        x3 = tf.nn.leaky_relu(x3)

        h2, w2 = tf.shape(x3)[1], tf.shape(x3)[2]
        x4 = tf.image.resize_bilinear(x3, (h2*2, w2*2))
        x4 = slim.convolution2d(x4+x0, channel, [3, 3], activation_fn=None)
        x4 = tf.nn.leaky_relu(x4)
        x4 = slim.convolution2d(x4, 3, [7, 7], activation_fn=None)
        
        return x4

if __name__ == '__main__':
    

    pass